Online analytical processing (OLAP) is an integral part of most data warehouse and business analysis systems. OLAP services provide for fast analysis of multidimensional information. For this purpose, OLAP services provide for multidimensional access and navigation of the data in an intuitive and natural way, providing a global view of data that can be “drilled down” into particular data of interest. Speed and response time are important attributes of OLAP services that allow users to browse and analyze data online in an efficient manner. Further, OLAP services typically provide analytical tools to rank, aggregate, and calculate lead and lag indicators for the data under analysis.
In OLAP, information is viewed conceptually as cubes, consisting of dimensions, levels, and measures. In this context, a dimension is a structural attribute of a cube that is a list of members of a similar type in the user's perception of the data. Typically, hierarchy levels are associated with each dimension. For example, a time dimension may have hierarchical levels consisting of days, weeks, months, and years, while a geography dimension may have levels of cities, states/provinces, and countries. Dimension members act as indices for identifying a particular cell or range of cells within a multidimensional array. Each cell contains a value, also referred to as a measure, or measurement.
One issue regarding the design of multidimensional databases is how to navigate in the multidimensional space. Since a multidimensional database is organized hierarchically, navigation in the multidimensional space has been limited to hierarchical navigation. However, a user may desire to navigate a dimension by members in a pattern that is not hierarchical. For example, a user may desire to move across parallel periods in a time dimension (e.g., from September Q3 2003 to September Q3 2004). This type of navigation has not been simple, requiring a user to navigate the hierarchy through a series of button clicks to reach the desired time period.